Using multiple topic models without determining their relatedness offers limited value. It becomes more difficult to find and discover a feature of interest as the collective knowledge continues to be digitized and stored in the form of news, blogs, Web pages, scientific articles, books, images, sound, video, and social networks. New computational tools to help organize, search, and understand these vast amounts of information are needed. Current tools to work with online information include search and links, automated and human generated topics.
One of the limitations of automatically generating topics is that models may be too broad or too specific, decreasing analytics accuracy that may be achieved as a result. In addition, automatically generated topics do not strongly define any taxonomy, ontology or other semantic meaning
One of the limitations of human generated topics is they have human-bias, thus, topics and taxonomies represent a specific viewpoint. Human generated topics and topic taxonomies may be expensive and time consuming to create and maintain. In addition to being difficult and expensive to create and maintain, human generated topics may not meet the needs of various users.
Therefore, there is still a need for automatically discovering related topics from a corpus ranging from broad topics to more specific topics based on the content of a large corpus. Automatically discovered topic relationships can improve search results, content navigation and provide increased precision for text analytics tasks including entity disambiguation and document linking using the discovered topic relationships.